A significant amount of work has been carried out in the art to identify “cancer signatures”, which can be used in patient management or which can identify the targets subverted in neoplasia. These efforts are mainly concentrated on unbiased screening of cancer transcriptomes. For example, one approach is to identify genes whose expression is significantly modified in tumours as compared to normal cells, or in tumours of different grades (e.g., Beer, D. G., et al. 2002, Nature Medicine Vol. 8, No. 8, 816-824) and to select from these a subset which are associated with survival. A difficulty of this approach is that the resultant signatures often represent the end point of complex upstream interactions, and cannot readily be allocated to particular molecular pathways.
Another approach has been used in Brown P O et al (Chang, H. Y., et al. 2004, PloS Biol. 2004 Feb. 2(2): E7). Here, gene expression profiles were obtained from fibroblasts, in response to serum exposure. Genes which formed part of this fibroblast common serum response were found to be regulated in many human tumours. It was proposed that this is due to similarity in the molecular mechanism of cancer progression and wound healing.
Signatures produced in the prior art are often not highly robust, and often fail to provide good results from datasets that have been obtained in different clinical environments and from different patients. Additionally, prior art signatures often include a large number of genes, which increases the cost and difficulty of clinical screening in patients. In a clinical setting the use of small signatures is desirable as analysis is rendered much easier. For instance, smaller numbers of genes are amenable to analysis with readily available technology such as Real Time PCR.
Therefore, there is a continuing need to develop new approaches to identifying cancer signatures, so as to identify new diagnostic, prognostic or therapeutic markers.